We present and analyse an algorithm that quickly finds falsifying inputs for hybrid systems. Our method is based on a probabilistically directed tree search, whose distribution adapts to consider an increasingly fine-grained discretization of the input space. In experiments with standard benchmarks, our algorithm shows comparable or better performance to existing techniques, yet it does not build an explicit model of a system. Instead, at each decision point within a single trial, it makes an uninformed probabilistic choice between simple strategies to extend the input signal by means of exploration or exploitation. Key to our approach is the way input signal space is decomposed into levels, such that coarse segments are more probable than fine segments. We perform experiments to demonstrate how and why our approach works, finding that a fully randomized exploration strategy performs as well as our original algorithm that exploits robustness. We propose this strategy as a new baseline for falsification and conclude that more discriminative benchmarks are required.

中文翻译：

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使用自适应概率搜索伪造混合系统

我们提出并分析了一种算法，该算法可以快速找到混合系统的伪造输入。我们的方法基于概率定向树搜索，其分布适应于考虑输入空间的越来越细粒度的离散化。在使用标准基准的实验中，我们的算法显示出与现有技术相当或更好的性能，但它没有建立系统的明确模型。相反，在单个试验中的每个决策点，它会在简单策略之间做出不知情的概率选择，以通过探索或利用来扩展输入信号。我们方法的关键是输入信号空间被分解成多个级别的方式，这样粗略的片段比精细的片段更有可能。我们进行实验来证明我们的方法如何以及为什么有效，发现完全随机的探索策略与我们利用鲁棒性的原始算法一样好。我们建议将此策略作为伪造的新基准，并得出结论，需要更多的判别基准。